Benchmarking and Comparing AD-Related AI Methods Across Sites on a Standardized Dataset

在标准化数据集上跨站点对 AD 相关 AI 方法进行基准测试和比较

• 批准号: 10825403
• 负责人: Christos Davatzikos
• 金额: $ 35.75万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10825403
• 关键词: Acceleration; Address; Administrative Supplement; Algorithms; Alzheimer' s Disease; Alzheimer' s disease risk; Award; Benchmarking; Big Data Methods; Biological; Biological Markers; Clinical; Clinical Data; Clinical Trials; Cognitive; Collaborations; Communities; Coupled; Data; Data Collection; Data Set; Data Sources; Databases; Dedications; Dimensions; Disease; Funding; Genetic; Genetic Diseases; Genetic Markers; Genomics; Goals; Grant; Image; Informatics; International; Leadership; Literature; Machine Learning; Magnetic Resonance Imaging; Methods; Modeling; Molecular; Outcome Measure; Parents; Pharmaceutical Preparations; Process; Prognosis; Research; Site; Software Tools; Speed; Standardization; Structure; Suggestion; Systems Analysis; Testing; Training; U-Series Cooperative Agreements; United States National Institutes of Health; Vision; Work; analytical method; analytical tool; artificial intelligence algorithm; biobank; brain magnetic resonance imaging; clinical predictors; cognitive system; data harmonization; data integration; data standards; deep learning; design; drug development; drug repurposing; endophenotype; experience; genetic association; genome wide association study; genomic data; imaging genetics; large scale data; machine learning method; neurobiological mechanism; neuroimaging; novel; outcome prediction; phenotypic data; prediction algorithm; repository; response; synergism; tool; translational impact; whole genome

Project Summary In response to PAR-19-269 “Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data (U01 Clinical Trial Not Allowed)”, our project unites experts in AD genomics, machine learning and AI (including deep learning), large-scale data integration, and international data harmonization to work in a carefully-designed Consortium Structure in close partnership with the NIH, ADSP, and NIAGADS. We will develop a suite of complementary big data analytic approaches for ultra-scale analysis of Alzheimer’s Disease (AD) genomic and phenotypic data. The vast data volumes now generated by the Alzheimer’s Disease Sequencing Project (ADSP), National Alzheimer’s Coordinating Center (NACC), Alzheimer’s Disease Neuroimaging Initiative (ADNI), Accelerating Medications Partnership AD (AMP-AD), and UK Biobank (UKBB), far exceed the capacity of all current analytic methods, which have not kept pace with the scale and speed of data collection. This vast amount of genetic and phenotypic data mandates new and more powerful algorithms to: (1) store, manage, and manipulate whole-genome sequences and associated data on an ever-growing scale; (2) discover novel AD risk and protective loci by merging informatics and AD genomics databases; (3) relate whole-genome changes to the ATN(v) biomarkers that now define biological AD. Our Ultrascale Machine Learning Initiative, or “ULTRA” - will offer new AI and deep learning tools to discover features in massive scale genomics data - relating whole genome data to biomarker features by merging all relevant data sources. Our team of experienced PIs will coordinate efforts across the U.S. to create these large-scale data analytic tools. Our MPI team and 6 Core Leads have decades of experience working together and with the AD community in pioneering machine learning methods for AD genetics and neuroimaging, including leadership of international neuroimaging consortia across the world. Dedicated Cores focus on Genomic, Imaging, and Cognitive Data Harmonization. Curated data will then be efficiently imported into AI approaches and informatics pipelines that will allow the AD research community to leverage ultra-scale, multidimensional genomic and phenotypic data from the ADSP, NACC, ADNI, AMP-AD, and others. Our work is organized by a carefully-designed and coordinated Consortium guided by all stake-holders, clinical leaders, and pioneering analysts in AD genomics and neuroimaging. Our ultrascale AI tools will advance AD genomics research and will include efforts in training, and a dedicated Drug Repurposing Core. This team effort will accelerate understanding of the genetic, molecular and neurobiological mechanisms of AD, yielding significant translational impact on disease and drug development.

项目摘要 响应PAR-19-269《阿尔茨海默病遗传和表型数据的认知系统分析》 (U01临床试验不允许)“，我们的项目联合了AD基因组学、机器学习和人工智能(包括 深度学习)、大规模数据集成和国际数据协调在精心设计的 与NIH、ADSP和NIAGADS密切合作的财团结构。我们将开发一套 用于阿尔茨海默病(AD)基因组和蛋白质组超大规模分析的互补大数据分析方法 表型数据。阿尔茨海默病测序项目(ADSP)现在产生的海量数据， 国家阿尔茨海默病协调中心(NACC)，阿尔茨海默病神经成像倡议(ADNI)， 加速药物伙伴关系AD(AMP-AD)和英国生物库(UKBB)，远远超过所有 现有的分析方法跟不上数据收集的规模和速度。如此庞大的数量 遗传和表型数据要求新的和更强大的算法：(1)存储、管理和 在不断增长的规模上操纵全基因组序列和相关数据；(2)发现新的AD风险 通过合并信息学和AD基因组学数据库来保护基因座；(3)将全基因组变化与 现在定义生物性AD的ATN(V)生物标志物。我们的UltraScale机器学习计划，或“ULTRA”- 将提供新的人工智能和深度学习工具，以发现与大规模基因组数据相关的整体特征 通过合并所有相关数据源，将基因组数据转换为生物标记物特征。我们经验丰富的私家侦探团队将 协调全美各地的努力，创建这些大规模的数据分析工具。我们的MPI团队和6核 Leads在开创性的机器学习方面拥有数十年的合作经验，并与AD社区合作 阿尔茨海默病遗传学和神经成像方法，包括领导国际神经成像联盟 整个世界。专门的核心专注于基因组、成像和认知数据的协调。经过管理的数据将 然后高效地导入到人工智能方法和信息管道中，这将允许AD研究 社区利用来自ADSP、NACC、ADNI、 AMP-AD和其他。我们的工作是由一个精心设计和协调的联盟组织的，由所有 AD基因组学和神经成像领域的股东、临床领导者和开拓性分析师。我们的UltraScale AI 工具将促进AD基因组学研究，并将包括在培训方面的努力，以及专门的药物再利用 核心。这一团队的努力将促进对遗传、分子和神经生物学机制的理解 对疾病和药物开发产生重大的翻译影响。

项目成果

Sex Differences in Cognitive Decline in Subjects with High Likelihood of Mild Cognitive Impairment due to Alzheimer's disease.

• DOI: 10.1038/s41598-018-25377-w
• 发表时间: 2018-05-10
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Sohn D;Shpanskaya K;Lucas JE;Petrella JR;Saykin AJ;Tanzi RE;Samatova NF;Doraiswamy PM
• 通讯作者: Doraiswamy PM

Attention-based multiple instance learning with self-supervision to predict microsatellite instability in colorectal cancer from histology whole-slide images.

• DOI: 10.1109/embc48229.2022.9871553
• 发表时间: 2022-07-01
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Leiby, Jacob S;Hao, Jie;Kim, Dokyoon
• 通讯作者: Kim, Dokyoon